Non-Intrusive Load Monitoring by Load Trajectory and Multi-Feature Based on DCNN

Yin, Hui; Zhou, Kaile; Yang, Shanlin

doi:10.1109/tii.2023.3240924

Cited by 9 publications

(3 citation statements)

References 37 publications

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“…The latest state-of-the-art approaches for NILM are based on DL algorithms, as shown in Table 1. In the reviewed works, Convolutional Neural Networks (CNN) [27], [28], [33] and Recurrent CNNs (CRNN) [15], [16], [19] are the most common choice. However, a variety of other algorithms are also used, e.g.…”

Section: B Methods For Solving Nilm Problemsmentioning

confidence: 99%

“…The ON/OFF classification of appliances aims to determine which devices are active and which inactive in an aggregated power signal [11], [16], [19], [29], [30]. The appliance classification problem also assumes that the disaggregated signals are accessible and intends to classify the devices that generated each unique power signature extracted from the NILM signal [27], [28], [32], [33]. The focus of this paper is on the ON/OFF classification problem type, which pertains to the identification of the activity state of individual appliances from an aggregated power signal without requiring prior disaggregation.…”

Section: A Nilm Problem Typementioning

confidence: 99%

“…Typically, the number of devices employed in different works is fixed, with the exception of Raiker et al [26], Chen et al [28] and Yin et al [33], who utilized up to 11, 15 and 5 devices, respectively. In this work, however, we utilize a flexible range of up to 54 devices.…”

Section: Nilm Data For ML Model Trainingmentioning

confidence: 99%

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Energy Efficient Deep Multi-Label ON/OFF Classification of Low Frequency Metered Home Appliances

Pirnat,

Bertalanič,

Cerar

et al. 2024

IEEE Access

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Non-intrusive load monitoring (NILM) is the process of obtaining appliance-level data from a single metering point, measuring total electricity consumption of a household or a business. Appliance-level data can be directly used for demand response applications and energy management systems as well as for awareness raising and motivation for improvements in energy efficiency. Recently, classical machine learning and deep learning (DL) techniques became very popular and proved as highly effective for NILM classification, but with the growing complexity these methods are faced with significant computational and energy demands during both their training and operation. In this paper, we introduce a novel DL model aimed at enhanced multi-label classification of NILM with improved computation and energy efficiency. We also propose an evaluation methodology for comparison of different models using data synthesized from the measurement datasets so as to better represent real-world scenarios. Compared to the state-of-the-art, the proposed model has its energy consumption reduced by more than 23% while providing on average approximately 8 percentage points in performance improvement when evaluating on data derived from REFIT and UK-DALE datasets. We also show a 12 percentage point performance advantage of the proposed DL based model over a random forest model and observe performance degradation with the increase of the number of devices in the household, namely with each additional 5 devices, the average performance degrades by approximately 7 percentage points. INDEX TERMS non-intrusive load monitoring (NILM), deep learning (DL), convolutional recurrent neural network (CRNN), multi-label classification, load profilingThis article has been accepted for publication in IEEE Access.

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